Information and communication technologies, e-Health and homelessness: A bibliometric review

Figures & data

Table 1. Search strategy used in the different databases

Data base	Search strategy
Pubmed	((“homeless persons”[Mesh] OR “homeless”[All Fields] OR “homelessness”[All Fields] OR “indigent”[All Fields]) AND (“information and communication technologies”[All Fields] OR “ICT”[All Fields] OR “computer*”[All Fields] OR “web 2.0”[All Fields] OR “online”[All Fields] OR “phone”[All Fields] OR “smartphone”[Majr:noexp] OR “internet”[Mesh] OR “social network site”[All Fields] OR “m-health”[All Fields] OR “mhealth”[All Fields]) AND (“0001/01/01”[PDAT]: “2016/12/31”[PDAT])) AND (hasabstract[text] AND “humans”[MeSH Terms])
PsycINFO	homeless persons”[Mesh] OR “homeless”[All Fields] OR “homelessness”[All Fields] OR “indigent”[All Fields]) AND (“information and communication technologies”[All Fields] OR “ICT”[All Fields] OR “computer*”[All Fields] OR “web 2.0”[All Fields] OR “internet”[All Fields] OR “online”[All Fields] OR “mobile phone”[All Fields]
Scopus	(((TITLE-ABS-KEY(“information and communication technologies”) OR TITLE-ABS-KEY(“computer”) OR TITLE-ABS-KEY(“internet”) OR TITLE-ABS-KEY(“online”) OR TITLE-ABS-KEY(“phone”) OR TITLE-ABS-KEY(“mobile phone”) OR TITLE-ABS-KEY(“smartphone”) OR TITLE-ABS-KEY(“mhealth”) OR TITLE-ABS-KEY(“ehealth”) OR TITLE-ABS-KEY(“ICT”)) AND ((TITLE-ABS-KEY(“homeless*”) OR TITLE-ABS-KEY(“indigent”))) AND ((LIMIT-TO(AFFILLANGUAJE, “English) AND ((LIMIT-TO(AFFILLANGUAJE, “Spanish)) AND ((LIMIT-TO(AFFILLANGUAJE, “Portuguese))
Scielo	(homeless OR homelessness OR indigent) AND (online OR internet OR social network sites OR web OR social network sites OR health OR m-health) AND la:(“en” OR “es”)
Homeless Hub	“web” OR “computer” OR “online” OR “mhealth” OR “m-health” OR “social network sites” OR “Information and Communication technologies” OR “mobile phone”

Figure 1. Article selection flow chart.

Figure 2. Evolution of number of publications.

Table 2. Context of investigation and location of principal authors and samples

	Filiations of authors		Sample locations
	University: schools, faculties and/or departments	Non-university institutions.	City (State); Country	Type of institutions of recruitment.
Asgary et al. (2015)	Public health	-	New York City (NY); USA	Shelter
Barman-Adhikari and Rice (2011)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Barman-Adhikari et al. (2016)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Bender et al. (2014)	Social work	-	Los Angeles (CA); USA	Shelter
Bender et al. (2015)	Social work	-	Los Angeles (CA); USA	Shelter
Buccieri and Molleson (2015)	Sociology	-	Toronto; Canada	Specific shelter for street youth
Burda et al. (2012)	Nursing	-	Baltimore (MD); USA	Street
Bure (2005)	Science and technology	-	Edinburgh and Glasgow; Scotland	Mental health services
Byrnes (2016)	Nursing	-	Elizabeth (NJ); USA	Urgency service for women
Curry et al. (2016)	Social work	-	No specified city or state (Western USA)	Drop-in agencies
Dang et al. (2012)	Nursing	-	Sacramento (CA); USA	Drop-in agencies
Eyrich-Garg (2010)	Social work	-	Philadelphia (PA); USA	Street
Eyrich-Garg (2011)	Social work	-	Philadelphia (PA); USA	Shelter
Fortin et al. (2015)	Public health	-	Toronto; Canada	Street
Freedman et al. (2006)	Psychology	-	No specified city (Alabama, USA)	Not reported
Guadagno et al. (2013)	-	Science foundation	New York City (NY) and Los Angeles (CA); USA	Shelter
Gui et al. (2016)	Engineering and informatics	-	Los Angeles (CA); USA	Soup kitchen
Harpin et al. (2016)	Nursing	-	Denver and Colorado Springs (CO); USA	Shelter, street and drop-in agency
Hendry et al. (2011)	Computer and information	-	Seattle (WA); USA	Drop-in agencies
Hersberger (2003)	Library and information	-	Indianapolis (IN), Seattle (WA) and Greensboro (NC); USA	Specific shelter for families
Jennings et al. (2016)	International health	-	Baltimore (MD) and Washington DC; USA.	Intervention program in unserved communities of homeless youth
Kelleher (2013)	-	Library	Lansing (MI); USA	Shelter
Kennedy et al. (2016)	-	Behavioral Policy Sciences	Los Angeles (CA); USA	Housing program
McInnes et al. (2014)	Public health	Veterans hospital	Providence (RI); USA	Veterans health centre
McInnes et al. (2014)	Public health	Veterans hospital	Providence (RI); USA	Primary care centre
McInnes et al. (2015)	Public health	Veterans hospital	Boston (MA); USA	Housing program
Miller et al. (2005)	Occupational therapy	-	Philadelphia (PA); USA	Long-stage shelter
Moser (2009)	Engineering and informatics	-	Calgary (Alberta); Canada	Shelter
Muggleton and Ruthven (2012)	Computers and information	-	Glasgow; Scotland	Shelter and street
Neale and Brown (2015)	-	Addiction service	No specified cities (England)	Shelter
Neale and Stevenson (2014)	-	Addiction service	No specified cities (England)	Shelter
Neale and Stevenson (2014a)	-	Addiction service	No specified cities (England)	Shelter
Neale and Stevenson (2014b)	-	Addiction service	No specified cities (England)	Shelter
Pollio et al. (2013)	Social work	-	Denver (CO) and Los Angeles (CA); USA	Shelter, street and drop-in agency
Post et al. (2013)	Medicine	-	New Haven and Bridgeport (CT); USA	Health emergencies
Redpath et al. (2006)	Behavioral research and services	-	Long Beach (CA); USA	Infectious disease centre
Rice (2010)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Rice and Barman-Adhikari (2014)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Rice et al. (2011)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Rice et al. (2011)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Rice et al. (2010)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Rice et al. (2012)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Rice et al. (2012)	Social work	-	Los Angeles (CA); USA	Drop-in agencies
Sheoran et al. (2016)	-	Technology development	Oakland (CA); USA	Shelter
Stennett et al. (2012)	Medicine	-	Los Angeles (CA); USA	Feed service
Swahn et al. (2014)	Public health	-	Kapala; Uganda.	Shelter
Taylor and Narayan (2016)	Technology	-	Sydney; Australia	Online recruitment
Vázquez et al. (2015)	-		Madrid; Spain	Shelter and street
Woelfer and Hendry (2011)	Computers and information	-	Seattle (WA); USA	Shelter and street
Young and Rice (2011)	Infectious diseases	-	Los Angeles (CA); USA	Drop-in agencies

Table 3. Sample, gender and age of participants of the selected articles

	SP*	Sample and gender				Age M, ED (Rang)
		n	Men n (%)	Women n (%)	Trans. n (%)
Asgary et al. (2015)	A	50	21 (42)	29 (58)	-	51.7,11.3 (25–79)
Barman-Adhikari and Rice (2011)	Y	169	114 (68.2)	53 (31.8)	-	20.9, 2.1 (13–24)
Barman-Adhikari et al. (2016)	Y	1,046	735 (70.3)	275 (26.3)	36 (3.4)	21.3, 2.16 (13–25)
Bender et al. (2014)	Y	98	60 (61.2)	36 (36.7)	3 (3.1)^a	19.0, 0.8 (17–20)
Bender et al. (2015)	Y	48	32 (66.7)	15 (31.3)	1 (2.1)^a	19.1, 0.7 (18–20)
Buccieri and Molleson (2015)	Y	12	NR^b	NR	NR	(18–23)^c,d
Burda et al. (2012)	M	10	8 (80)	2 (20)	-	(21–64)^c,d
Bure (2005)	A	16	15 (93.7)	1 (6.3)	-	30.2^d,e
Byrnes (2016)	W	10	-	10 (100)	-	(18–21)^c,d
Curry et al. (2016)	Y	539	391 (72.4)	159 (27.6)	-	21.1, 1.9 (14–24)
Dang et al. (2012)	Y	149	76 (51)	73 (49)	-	(9–24) ^c,d
Eyrich-Garg (2010, 2011)^f	A	100^b	73 (73.0)	27 (27.0)	-	45, 10.0^e
Fortin et al. (2015)	W	5	-	5 (100)	-	20.2, 2.28 (18–24)
Freedman et al. (2006)	M	30	11 (37)	19 (63)	-	38, 6.2^e
Guadagno et al. (2013)	Y	86	31 (36.0)	54 (62.8)	1 (1.2)	19.4, 1.09^e
Gui et al. (2016)	A	14	NR	NR	NR	(17–70)^c,d
Harpin et al. (2016)	Y	18^g	133 (73.5)	41 (22.7)	7 (3.8)	20.6, 0.2^e
Hendry et al. (2011)	Y	75	NR	NR	NR	(13–25)^c,d
Hersberger (2003)	F	25	NR	NR	NR	NR
Jennings et al. (2016)^Citationh	Y	52	21 (40.4)	31 (59.6)	-	21.4^d,e
Jennings et al. (2016)^Citationh		41	11 (26.8)	30 (73.2)	-	18.1, 0.4 (15–24)
Kelleher (2013)	A	121	NR	NR	NR	(<21–50)^c,d
Kennedy et al. (2016)	A	60	74%^i	26%^i	-	NR
McInnes et al. (2014); McInnes et al. (2014)^Citationf	M	21	17 (81.1)	3 (15.0)	1 (4.9)	55^d (25–68)
McInnes et al. (2015)	M	30	26 (86.6)	3 (13.4)	-	53.6, 8.3 (33–65)
Miller et al. (2005)	A	7	7 (100)	-	-	35^d (21–47)
Moser (2009)	A	13^j	NR	NR	NR	NR
Muggleton and Ruthven (2012)	A	18	18 (100)	-	-	NR
Neale & Stevenson (2014, 2014a, 2014b)^f	M	30^k	25 (83.3)	5 (16.7)	-	43^d (23–62)
Neale and Brown (2015)	M	30^l	21 (70.0)	9 (30.0)	-	38^d (21–54)
Pollio et al. (2013)	Y	100	67 (67.0)	33 (33.0)	-	20.4, 1.8 (18–24)
Post et al. (2013)	A	249^m	136 (54.6)	113 (45.4)		40.0^d (18->65)
Redpath et al. (2006)	M	265^n	186 (70.2)	79 (29.8)	-	43.6, 8.7^e
Rice (2010)	Y	103	60 (58.3)	43 (41.8)	-	20.9, 2.2 (16–26)
Rice and Barman-Adhikari (2014)	Y	194	128 (66.0)	66 (34.0)	-	21.1, 2.1^e
Rice et al. (2011)	Y	169	111 (65.7)	58 (34.3)	-	20.9, 2.1 (13–24)
Rice et al. (2011)	Y	136	81 (60.5)	55 (39.5)	-	(16–25) ^c,d,°
Rice et al., 2010; Young and Rice (2011)^Citationf	Y	201	133 (66.2)	62 (30.8)	6 (3.0)	21, 2.1 (13–24)
Rice et al. (2012)	Y	136	81 (60.5)	53 (39.6)^p	-	20.8, 2.1(13–24)
Rice et al. (2012)	Y	60^q	37 (61.7)	23 (38.3)	-	22.8, 1.8^e
Sheoran et al. (2016)	Y	6	3 (50.0)	3 (50.0)	-	(18–25)^c,d
Stennett et al. (2012)	A	39	NR	NR	NR	NR
Swahn et al. (2014)	Y	415	129 (31.1)	284 (68.4)	-	(14–24)^c,d,r
Taylor and Narayan (2016)	-	1	NR	NR	NR	NR
Vázquez et al. (2015)	A	188	158 (84.0)	30 (16.0)	-	47.57, 12.2^e
Woelfer and Hendry (2011)	Y	80	NR	NR	NR	(13–25)^c,d

NR = Not referred

*SP = Subpopulation of homelessness A = Adults, Y = Homeless youth, teenagers and young adults, M = Mental health and addictions, W = Homeless women pregnant or young mothers, F = Families.

^aTranssexual not specified, “other” use instead.

^bNot reported.

^cMean not reported.

^dStandard deviation not reported.

^eRang not reported.

^fArticles that use the same sample.

^gThe initial sample was 191, but 10 cases were excluded for not following inclusion criteria.

^hStudy which has two samples. One first sample of participants distributed in 9 focus group (n = 52) and a second sample of participants in individual interviews (n = 41).

ⁱAbsolute number not specified, only the percentage.

^jThe total of the sample was 42 but the rest were not IEH.

^kThe sociodemographic data were obtained in a first interview. In the second interview, which provides some results, 22 IEH participate.

^mSample of IEH compared with 5,539 non-IEH (accident and emergency patients not included in this description).

ⁿIt is reported that only 230 reported their IEH condition.

^oThe authors claim that 83.7% of the sample are between ages of 18 and 22 years old.

^pData reported about gender not clear.

^qThe initial sample is 163 but only 60 people were IEH.

^rAuthors specify: 42.1% (n = 175) are <18 years, and 57.1% (n = 237), ≤18.

Table 4. Objectives of the research

	Objectives of the papers included in the review
Asgary et al. (2015)	Evaluate the perceptions the experience of the use of mobile phones in health care.
Barman-Adhikari and Rice (2011)	Evaluate the use of Internet and SNS to search information on sexual health.
Barman-Adhikari et al. (2016)	Determine the prevalence of use of SNS and type of connections and conversations in relation to risk/protective sexual behaviour.
Bender et al. (2014)	Evaluate the usefulness of ICT to increase retention in longitudinal studies and their connection preferences.
Bender et al. (2015)	Evaluate a pilot test of the electronic youth management to homeless youth.
Buccieri and Molleson (2015)	Describe an experience about their participation in the design of an app to increase the Internet use of other IEH.
Burda et al. (2012)	Evaluate the reliability of mobile phones to monitor adherence to pharmacological treatment in mental health.
Bure (2005)	Investigate how ICT are used in daily life and how this affects their social integration.
Byrnes (2016)	Determine whether the Text4baby app is seen as adequate to provide health information to young mothers.
Curry et al. (2016)	Determine what factors predict the use of the Internet to seek housing, employment or health resources compared to face-to-face.
Dang et al. (2012)	Check the acceptability of a historical online clinical system.
Eyrich-Garg (2010)	Describe the use of mobile phones and their influence on access to social support networks.
Eyrich-Garg (2011)	Analyze the use of computers and the potential benefits or risks in relation to their quality of life.
Fortin et al. (2015)	Explore the most relevant topics about the conditions of life as IEH.
Freedman et al. (2006)	Analyze the feasibility of using mobile phones in the treatment of cocaine addicts.
Guadagno et al. (2013)	To compare if the use of SNS is similar between IEH and university students of the same age.
Gui et al. (2016)	Determine the degree of possession of digital devices, access to ICT and their influence on their lives.
Harpin et al. (2016)	Explore the prevalence of mobile phone use and social media use.
Hendry et al. (2011)	Evaluate ICT skill training and its impact on personal and emotional skills and competencies.
Hersberger (2003)	Know the needs of access to information and ICT resources in their daily lives.
Jennings et al. (2016)	Examine their access and use to mobile phones, and collect their preferences and suggestions for the design of an mHealth intervention.
Kelleher (2013)	Examine the use of the services offered by libraries.
Kennedy et al. (2016)	Improve motivation to reduce drug use and reduce HIV risk behaviors through an online motivational program
McInnes et al. (2014)	Analyze the reliability of the use of text messaging with mobile phones to increase the retention rate to health services.
McInnes et al. (2014)	Develop a text message system for mobile phones to increase attendance of scheduled visits to primary care services.
McInnes et al. (2015)	Determine the accessibility to ICT of veterans and their interest to communicate in this way with health services.
Miller et al. (2005)	Explore the experience of using computers and their meaning after a job placement workshop (occupational therapy).
Moser (2009)	Understand how they adopt technology and how the production of online texts can be related to the personal structure.
Muggleton and Ruthven (2012)	Explore how ICT access affects and how it can be related to the formation of identity and social interaction.
Neale and Brown (2015)	Explore participation through ICT and its potential capacity in the recovery of drug dependence and online treatment.
Neale and Stevenson (2014)	Explore the characteristics of a therapy in addictions online through the computer.
Neale and Stevenson (2014a)	Explore the acceptance of online therapy in drug addiction, assisted by computer.
Neale and Stevenson (2014b)	Explore the role that friendship plays in your life and how ICTs get involved in these relationships.
Pollio et al. (2013)	Explore their use of technology and what risk factors associated with homelessness predict such use.
Post et al. (2013)	Compare the use of the mobile phone after going to the emergency room and the demands of health information.
Redpath et al. (2006)	Describe Internet access to determine the effectiveness potential of online interventions.
Rice (2010)	Examine whether the relationship through SNS with other young people with healthy behaviors reduces exposure to risky sexual behaviors.
Rice and Barman-Adhikari (2014)	Establish what type of connections they make and how they influence the search for online resources.
Rice et al. (2011)	Examine the prevalence of mobile phone use and the health implications of its social and instrumental use.
Rice et al. (2011)	Examine how the differences in the composition of the social bond may be related to drug use.
Rice et al. (2010)	Analyze the association between sexual health and the use of internet and SNS to find a sexual partner.
Rice et al. (2012)	Analyze if the integration in street and home-based networks with the help of ICT improve the results in anxiety and depression.
Rice et al. (2012)	Examine the acceptability of an HIV prevention program through the SNS.
Sheoran et al. (2016)	Develop a mobile application to improve access to health resources.
Stennett et al. (2012)	Determine the predominant behavior for the search of information and the most efficient contact method with health services.
Swahn et al. (2014)	Determine the prevalence of mobile phones and the psychosocial characteristics that differentiate between those who have and those who do not.
Taylor and Narayan (2016)	Follow the activity on Twitter of an IEH to determine the type of use it makes of the social network.
Vázquez et al. (2015)	Analyze access to ICT and its main uses.
Woelfer and Hendry (2011)	Determine if access to ICT improves the chances of “escaping” from those conditions in relation to the classic information system.
Young and Rice (2011)	Analyze the relationship between seeking sexual partners through SNS with risk behaviors of transmission of viral diseases.

Table 5. Prevalence of use of ICT, dispositive and/or Internet

	Computers	Mobile phones	Internet	SNS
Asgary et al. (2015)	-	78% has a working cell with the possibility to send and receive text messages.	-	-
Barman-Adhikari and Rice (2011)	61.0% used it on some occasion to search health 61.0%, 47.3% about STD, 40.7% about sex and 23.% about testing of VIH.	-	54.4% every day	66.4% connected with housemates, 52.7% street peers, 47.9% with online acquaintances and 34.1% with parents.
Barman-Adhikari et al. (2016)	-	-	-	79.2 used them on some occasions. 32.7% several times a day or daily, 23.9% several times a week or once a week, 19.1% less than once a week and 20.7% never. 56.1% with friends from home, 49.7% with family, 36.1% with Street friends and 33.1% with boyfriends, girlfriends or partners.
Curry et al. (2016)	-	-	72.5 % used it periodically.	-
Eyrich-Garg (2010)	-	44% have a mobile and 35% are owners. Use to communicate with family and friends.	9% access through the mobile.
Eyrich-Garg (2011)	47% used the computer in the last month. 26% to search employment, 18% for leisure or surf the Internet, 7% to have access to SNS, 7% to edit or process texts 5% to look for housing.	-	47% use it habitually.	7% accessed SNS habitually
Guadagno et al. (2013)	-	-	-	75% used them periodically.
Harpin et al. (2016)	-	46.7% owned a mobile phone and 29.3% owned a smartphone.	-	71.8% used SNS (Facebook). 12.2% used Twitter and 10.0% other SNS.
Hersberger (2003)	76% did not know how to use the computer. 24% used it occasionally to search information.	-	24.0% on some occasion.
Jennings et al. (2016)	-	85.0% had a mobile. From the 32 persons that had a mobile, 30 used SMS, 29 multimedia messages and 27 Internet.	-	-
McInnes et al. (2015)	-	90% had one, of which 30% were smartphones. Mainly they are used to keep contact with friends and family, check appointments on the calendar, find work^a.	70% used Internet to keep in contact with friends and family, manage work issues, look at bank accounts, contact health services and leisure. 72% had email.	-
Miller et al. (2005)	57.1% had never used a computer until the course began.	-	-	-
Moser (2009)	Use for leisure, connect with friends and family, reduce stigma associated to homelessness, surf on internet listen to music, find work, develop small personal projects,training courses, and look for work. ^a	-	-	-
Muggleton and Ruthven (2012)	Objective to communicate and spend leisure time and find information^a.	-	-	-
Neale and Stevenson (2014)	66.0% used the computer every day. 6.7% had never used it. 23.3% had their own laptop.	86.7%had a mobile phone, 20% of which were smartphones	-	33% have active Facebook accounts, though they were not used very periodically.
Neale and Brown (2015)	-	It is the most common device to contact friends, especially by phone calls.	18.2% used internet to contact friends through SNS.	18.2% used Facebook mainly to contact other IEH who had moved and with whom direct contact had been lost.
Pollio et al. (2013)	They use it an average of 2.8 days per week (ED 0 2.6).	6% have access to a mobile phone.	They connect an average of 4.6 days per week (ED = 2.5), and to check email an average of 3.8 days (ED = 2.7). The aim of 56% is to communicate, of 46% to look for work, and 36% leisure.	22% access to SNS an average of 3.8 days per week (ED = 2.8). 71% use it to contact friends and 55% the family.
(Post et al., 2013)	-	70.7% have their own phone, 30.5% smartphones. 70,2% use it to make calls, 50.6% to type SMS, 32.5% access the internet through their phone, 20.9% look for health information), 6.4%) use health apps 29.7%) send emails, 26.1% listen to music, 24.5% play games, 22,9% use apps, and 9.3% watch videos online.	59% use Internet regularly and 45.4% use email.	41.0% are SNS users and 26.9% use them through their mobile phone.
Redpath et al. (2006)	55.1% had used the computer before in their life. 24.6% had owned one.	-	19% have accessed internet in the last 30 days and 9.9% had sent or received an email in the last 30 days. 24.2% had email.
Rice (2010)	-	-	-	The use of SNS to interact with housemates was over 50%^a.
Rice and Barman-Adhikari (2014)	-	-	30.5% used it that same day, 31.1% the previous day, 10% two days before 12.1% some days before and 16.3% over a week before. 63.9% connected to check their email.	52.2% to check their SNS (Facebook, Myspace), 27.3% to watch videos on Youtube, 27.84% to look for work and 12.9% to look for housing.
Rice et al. (2011)	-	62% have a mobile phone (40% a work phone, 15% a phone without credit to call, 7% share with a friend). 100% use it at least once a day. The aim of 50.9% is to contact friends or people close to the home, 42.6% with siblings, cousins or other relatives, 41.4% with other relatives, 37.9% with street peers, 24.3% with potential employers, 23.1% with people they had met online, 17.2% with care staff (social workers) and 11.8% with current employers.	-	-
Rice et al. (2011)	-	-	75% use internet and SNS habitually.	43% connected with a home-based peer who did not consume drugs 31% with drug consuming home based peers. 32% contacted with non-consuming relatives and 18% had drug-consuming relatives in their social network.
Rice et al. (2010)	-	-	Use Internet on some occasion, 96.5%. 84% at least once a week. 44.8% use email to contact home based peer or home based friends, 36.8% to contact friends or friends made online, 42.3% with the extended family, 40.8%with Street based peers or Street based friends and 18.9% with parents.	18.9% used them to contact with direct family, 42.3% with extended family, 59.7% with home based friends, 40.8% with street based friends and 42.8% with people they have met online.
Rice et al. (2012)	-	-	-	Made an average of 1.54 (ED = 2.21) contacts online with home based peers, 0.59 (ED = 1.22) with home based friends, 0.57 (ED = 1.15) with street based peers and 0.38 (ED = 0.89) with street based friends.
Sheoran et al. (2016)	-	100% had a mobile phone, of which 83% were smartphones.	-	-
Stennett et al. (2012)	-	53.8% had a mobile phone.	-	-
Swahn et al. (2014)	-	45.5% had a mobile phone and used it daily 54. 5% did not have one or used it weekly or less often.	9,3% used internet and 5,3% had email.	4,9% used Facebook.
Vázquez et al. (2015)	-	75.4% of the 30.8% of the sample used it (up to 42 years old), 50.8% of 35.1% of the sample (43 to 52 years old) and 56.5% of the sample (over 52 years old).	70.2% of 30.8% of the sample used it (up to 42 years old), 32.3% of the 35.1% of the sample (43 to 52 years old) and 16.1% of the sample (over 52 years old). 59.6%, 32.3% and 12.9% respectively had email.	35.1% of 30.8% of the sample used it (up to 42 years old),17.2% of 35.1% of the sample (43 to 52 years old)and 1.6% of the sample (over 52 years old).
Young and Rice (2011)	-	-	-	78.7% connected weekly and 44.6% daily. 78.1% to Myspace, to 29.9% Facebook and 10% to Twitter (10.0%).

^a Percentages are not included.

Table 6. Summary of the main results and conclusions related with health and e-Health proposals

Topic		Principal results and conclusions.
Relationship between care-providers and IEH.	Burda et al. (2012)	The mobile phone improved adherence to medication with patients with mental health issues.
	Dang et al. (2012)	Online clinical history system was adequate to increase knowledge and management of health aspects.
	McInnes et al. (2014); McInnes et al. (2014)	App message text was efficient to increase presence in follow up medical appointments and reduced visits to A&E.
	K. McInnes et al. (2015)	Internet was useful to contact with IEH from the health services.
	Sheoran et al. (2016)	App to locate health services was considered useful by IEH.
Prevention of infectious diseases. Protection factors.	Barman-Adhikari and Rice (2011)	Receive information through SNS about health and contact with family were elements that increase protecting factors to catch VIH.
	Rice (2010)	Contact through SNS with people who used protective measures against risk of VIH contagion, the use of condom increased.
	Rice et al. (2010)	The contact through the SNS with the family or home-based friends, increased the protective factors that minimize the risk of HIV infection.
	Rice et al. (2012)	The use of SNS increased retention in a program that requires continuation to be effective in preventing the spread of sexual infectious diseases.
	Young and Rice (2011)	The contact through the SNS with family and friends decreased the risk behaviors of HIV infection, such as exchanging things by sex or not to test.
General mental health and psychology	Eyrich-Garg (2011)	The use of SNS was related to a lower presence of risk behaviors such as consuming drugs, considering HIE, sleeping on the street or time in a homelessness situation.
	Gui et al. (2016)	The use of the computer and the Internet mentally distanced the IEH from the harsh conditions of life on the street. The participants themselves described it as a protective factor in mental health.
	Miller et al. (2005)	The functional uses of the computer in IEH that had never used them increased the perception of self-esteem, self-efficacy and motivation.
	Muggleton and Ruthven (2012)	The use of the Internet improved general mental health: it relaxes, relieves and disconnects from life on the street. The perception of self-esteem increased.
	Neale and Stevenson (2014)	An increase in contacts in SNS with home-based friends was associated with a decrease in depression symptoms.
Drug addiction	Freedman et al. (2006)	The use of the mobile phone was considered effective to monitor the craving or other elements related to the disorder due to cocaine dependence and for the treatment.
	Kennedy et al. (2016)	Program through the SNS useful to reduce behaviors that negatively impact on health such as drug use and sexual risk behavior.
	Neale and Brown (2015)	The analysis of the potentialities of the use of ICT to incorporate in the treatment of drug addictions presented potentialities at the communicational level.
	Neale and Stevenson (2014)	The evaluation of a therapy directed to the treatment of addictions, assisted by computer, reported good results in efficacy and applicability.
	Neale and Stevenson (2014)	App of online treatment of drug addiction reduced the drug consumption of the participants.
	Rice et al. (2011)	The contact by SNS used to be with people with healthier behaviors. That contact was related to a lower consumption of drugs.
Women	Byrnes (2016)	Text4Baby app effective and effective with homeless-women to improve knowledge of pregnancy and upbringing of babies.

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