Daily wellness questionnaire for your squad

The daily wellness questionnaire is a short form the player completes before training. A common version has five items (fatigue, sleep, muscle soreness, stress and mood) on a scale of 1 to 5, and it is read against each player’s own baseline, not a shared threshold. The evidence supports it as a monitoring signal, but it is mixed and does not show that it predicts injury.

Why ask the player before you measure them

In a survey of 41 of the 82 invited high-level clubs in Europe, the US and Australia, players’ responses to training were monitored with questionnaires in 68% of clubs and with submaximal exercise protocols in 41%. The main barriers were limited human resources first, then coach buy-in (Akenhead & Nassis, 2016). Half of the invited clubs responded.

In another survey of 100 high-performance sport staff from several sports (55% response rate), self-report questionnaires were the most commonly used method for monitoring fatigue and recovery (84%), with methods based “mainly on experiential evidence rather than replication of methods used in scientific publications” (Taylor et al., 2012).

A systematic review of 56 studies found that subjective measures reflected acute and chronic training loads “with superior sensitivity and consistency than objective measures”, although subjective and objective measures “generally did not correlate” (Saw et al., 2016). They are read as a complement to GPS and to sRPE, not as a replacement.

What to ask: the five items

The McLean-style five-item form scores fatigue, sleep quality, muscle soreness, stress and mood from 1 to 5, where 5 is best. The anchors are “Very fresh … Always tired” for fatigue and “Very restful … Insomnia” for sleep (World Rugby Passport, secondary source). McLean et al. (2010) is a rugby league study of 12 players, not a football study: perceived fatigue, well-being and soreness were significantly reduced in the 48 hours after the match, and fatigue had returned to baseline by day 4 of the 5-day microcycle.

In football it is used in studies such as Oliveira et al. (2023), with 13 professionals over 16 weeks, and Malone et al. (2018), with one goalkeeper, which used 0.5-point steps and summed the five scores.

A review of 21 studies (500 athletes, 181 of them in football) shows there is no standard: 3 to 8 items, on 5-, 7- or 10-point scales. The most used were soreness (20 studies), fatigue (20), sleep (19), stress (14) and mood (6); in 14 studies the composite was summed (Duignan et al., 2020).

Hooper versus the five-item form

They are not the same thing, and it is best not to mix them:

Sources: Barčák (2024, secondary) for the Hooper definition; World Rugby Passport (secondary) for the 5-item form.

The origin is Hooper and Mackinnon (1995). Of 12 studies citing it, only 4 called the summed score the “Hooper Index”, and the direction of the scale varies: in 7 studies the lower score was the better one (Duignan et al., 2020).

How to implement it

In 30 interviews with athletes, coaches and support staff at a national sports institute, the factors influencing implementation related to the measure itself (accessibility, timing of completion) and to the social environment (buy-in, reinforcement). A well-designed measure should obtain quality data with minimal burden on athletes and staff (Saw et al., 2015).

Read it per player, not per squad

A raw score is not interpreted against a shared table. The individual baseline is the z-score: (score − player’s mean) / the player’s standard deviation, computed per item (Campbell et al., 2020). Malone et al. (2017) and Gallo et al. (2016) model it as a “wellness z-score”.

In 48 football players across two teams, a z-score of −1 was associated with −4.9% player load, −8.9% player load at low speed and about −3.5% high-speed distance (Malone et al., 2017). Gallo et al. (2016) saw a similar pattern in Australian football.

What the numbers show

• The day after the match. In Oliveira et al. (2023), MD+1 had the lowest wellness values, with large negative correlations between fatigue and sRPE (r = −0.593; n = 13). In Malone et al.’s (2018) goalkeeper it was lowest on MD+1 and MD−4 (a single player, small to moderate correlations).
• Fatigue and high-intensity running. In 10 players from a Premier League club academy over 17 days, daily fatigue correlated with the previous day’s high-intensity running (r = −0.51); soreness and sleep were not significant (Thorpe et al., 2015).
• Two-game weeks. In a Portuguese premier league sample (n = 35), muscle soreness was higher (3.33 vs 3.10) and so was fatigue (3.18 vs 2.99), with small effect sizes (0.087 and 0.060) (Clemente et al., 2017).

What it cannot tell you

• There is no evidence that it predicts injury. None of the sources consulted tests it, and Duignan et al. (2020) call for links to clinically meaningful outcomes.
• The relationship with load is contested. Associations with training load were “predominantly trivial to moderate in the studies with the largest numbers of observations”, and the relationship “may not be expected or useful” (Duignan et al., 2020). In a laboratory study of 10 participants (a simulated match at three intensities), total wellness did not differ between trials (“limited capacity to differentiate training intensities”); only fatigue at 24 hours differed (Campbell et al., 2020).
• There is no validated threshold. A cut-off such as “below 3 is an alert” is not validated: methods are based mainly on experience (Taylor et al., 2012) and questionnaires vary from study to study (Duignan et al., 2020).
• It reflects more than training. It also picks up the player’s life stress (World Rugby Passport, secondary source).
• The evidence is not only from elite players. Thorpe et al. (2015) studied academy players and Campbell et al. (2020) non-professional participants.

Keeping players answering

Coach buy-in is among the main barriers in high-level clubs (Akenhead & Nassis, 2016). Saw et al. (2015) add that a supportive environment requires buy-in and coordination of all parties, at both the individual and the organizational level.

In practice

A reasonable weekly routine, based on what the evidence shows: fix the items and the scale once; compare each player against their own baseline; read wellness alongside sRPE and GPS; and review with the medical team the cases that depart from the usual, without treating the number as a diagnosis. In Athlo, daily wellness is configured with numeric or single-choice questions and visual thresholds , the player completes it from their phone or from a shared locker-room tablet , and visual alerts flag responses that need attention . In the load dashboard, staff view it next to the physical GPS indicators. You can see how it works in Wellbeing and sRPE.

Sources

• Akenhead R, Nassis GP (2016) “Training load and player monitoring in high-level football: current practice and perceptions” International Journal of Sports Physiology and Performance 11(5):587–593.
• Taylor K-L, Chapman DW, Cronin JB, Newton MJ, Gill N (2012) “Fatigue monitoring in high performance sport: a survey of current trends” Journal of Australian Strength and Conditioning 20(1):12–23.
• McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ (2010) “Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players” International Journal of Sports Physiology and Performance 5(3):367–383.
• Hooper SL, Mackinnon LT (1995) “Monitoring overtraining in athletes. Recommendations” Sports Medicine 20(5):321–327.
• Duignan C, Doherty C, Caulfield B, Blake C (2020) “Single-item self-report measures of team-sport athlete wellbeing and their relationship with training load: a systematic review” Journal of Athletic Training 55(9):944–953.
• Oliveira RFS, et al. (2023) “The relationship between wellness and training and match load in professional male soccer players” PLOS ONE.
• Malone JJ, Jaspers A, Helsen WF, Merks B, Frencken WGP, Brink MS (2018) “Seasonal training load and wellness monitoring in a professional soccer goalkeeper” International Journal of Sports Physiology and Performance 13(5):672–675. https://doi.org/10.1123/ijspp.2017-0472
• World Rugby Passport, wellness questionnaires (educational page, secondary source).
• Barčák P (2024) “Application of the Hooper Index in soccer” (theoretical review, secondary source).
• Saw AE, Main LC, Gastin PB (2016) “Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review” British Journal of Sports Medicine 50:281–291.
• Thorpe RT, Strudwick AJ, Buchheit M, et al. (2015) “Monitoring fatigue during the in-season competitive phase in elite soccer players” International Journal of Sports Physiology and Performance 10(8):958–964.
• Malone S, Owen A, Newton M, et al. (2017) “Wellbeing perception and the impact on external training output among elite soccer players” Journal of Science and Medicine in Sport.
• Gallo TF, Cormack SJ, Gabbett TJ, et al. (2016) “Pre-training perceived wellness impacts training output in Australian football players” Journal of Sports Sciences.
• Campbell PG, Stewart IB, Sirotic AC, et al. (2020) “Does exercise intensity affect wellness scores in a dose-like fashion?” European Journal of Sport Science.
• Clemente FM, Mendes B, Nikolaidis PT, et al. (2017) “Internal training load and its longitudinal relationship with seasonal player wellness in elite professional soccer” Physiology & Behavior.
• Saw AE, Main LC, Gastin PB (2015) “Monitoring athletes through self-report: factors influencing implementation” Journal of Sports Science and Medicine 14(1):137–146.